code: def _create_posterior(
      self,
      flat_hp_space_tuples: List[
          Tuple[str, HyperparameterDistribution]
      ],
      trials: List[TrialReport],
  ) -> List[HyperparameterDistribution]:
      # Loop through all hyperparams
      posterior_distributions: List[HyperparameterDistribution] = []
      for hp_key, hp_distribution in flat_hp_space_tuples:
          # Typing:
          hp_key: str = hp_key
          hp_distribution: HyperparameterDistribution = hp_distribution
  
          # Get these trials' hyperparam values for this hyperparam
          trial_hyperparams: List[HPSampledValue] = [
              trial.get_hyperparams()[hp_key] for trial in trials
          ]
  
          if hp_distribution.is_discrete():
              posterior_distribution: Union[
                  Choice, PriorityChoice
              ] = self._reweights_categorical(
                  hp_distribution, trial_hyperparams
              )
          else:
              posterior_distribution: DistributionMixture = (
                  self._create_gaussian_mixture(
                      hp_distribution, trial_hyperparams
                  )
              )
  
          posterior_distributions.append(posterior_distribution)
  
      return posterior_distributions
  
imports: from collections import Counter
  from operator import itemgetter
  from typing import Any, List, Tuple, Union
  import numpy
  from neuraxle.hyperparams.distributions import Choice, DiscreteHyperparameterDistribution, DistributionMixture, HPSampledValue, HyperparameterDistribution, LogSpaceDistributionMixin, PriorityChoice, Quantized
  from neuraxle.hyperparams.space import HyperparameterSamples, HyperparameterSpace
  from neuraxle.metaopt.data.reporting import RoundReport, TrialReport
  from neuraxle.metaopt.optimizer import BaseHyperparameterOptimizer, GridExplorationSampler
label_desc: Use image node_id 4 for calling the _DividedMixturesFactory obj's underlying member method code with example usage: obj._create_posterior(flat_hp_space_tuples, trials) and returns: posterior_distributions